How Google Cloud Solutions Help Retail Firms to ABP(Always Be Pivoting)
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For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.
And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.
Today’s retail landscape has surfaced both existing and new challenges for specialty and department store retailers. Approximately 88% of purchases previously occurred within a store environment. Now, it’s closer to 59%, with the remainder done online or through other omni methods.
With such constant change and upheaval, it can feel like the mantra now is ABP: always be pivoting.
The big question isn’t just how to maintain constant momentum and agility—it’s how to do it without sapping your workforce, your inventory, or your profits in the process. The pivot is now a given. What matters is how you do it.
Adapting requires a flexible base of technology that allows retailers to shift and scale seamlessly with the needs of the moment.
They need to be able to leverage real-time insights and enhance customer experiences rapidly, online and in the real world (not to mention the growing hybridization that’s AR and VR). They need to modernize their stores to power engaging consumer and associate experiences. They need to enhance operations for rapid scaling between full operations and digital-only offerings.
To help retailers achieve these goals and more, Google Cloud is honing a trio of essential innovations: demand forecasting that harnesses the power of data analytics and artificial intelligence; enhanced product discovery to improve conversion across channels; and the tools to help create the modern store experience.
In other words, here’s some of the biggest ways we’re ready to help you pivot.
Pivot point 1: Harnessing data and AI for demand forecasting with Vertex AI
One of the greatest challenges for retailers when building organizational flexibility is managing inventory and the supply chain.
We are in the midst of one of the worst global supply chain crises, stemming from soaring demand and logistics issues brought on by the pandemic. This crisis has only heightened the challenge retailers face when assessing demand and product availability. Even in normal times, mismanagement of inventory can add up to a trillion-dollar problem, according to IHL Group (costing $634 billion in lost sales worldwide each year, while overstocks result in $472 billion in lost revenues due to markdowns).
On the flipside, optimizing your supply chain can lead to greater profits. For instance, McKinsey predicts that a 10% to 20% improvement in retail supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues.
Some of the challenges related to demand forecasting include:
- Low accuracy leads to excess inventory, missed sales, and pressure on fragile supply chains.
- Real drivers of product demand are not included, because large datasets are hard to model using traditional methods.
- Poor accuracy for new product launches and products that have sparse or intermittent demand.
- Complex models are hard to understand, leading to poor product allocation and low return on investment on promotions.
- Different departments use different methods, leading to miscommunication and costly reconciliation errors.
AI-based demand forecasting techniques can help. Vertex AI Forecast supports retailers in maintaining greater inventory flexibility by infusing machine learning into their existing systems. Machine learning and AI-based forecasting models like Vertex AI are able to digest large sets of disparate data, drive analytics and automatically adjust when provided with new information.
With these machine learning models, retailers can not only incorporate historical sales data, but also use close to real-time data such as marketing campaigns, web actions like a customer clicking the “add to cart” button on a website, local weather forecasts, and much more.
Pivot point 2: Enhanced product discovery through AI-powered search and recommendations
If customers can’t easily find what they are looking for, whether online or at the store, they will turn to someone else. That’s a simple statement, but one with profound impacts.
In research conducted by The Harris Poll and Google Cloud, we found that over a six month period, 95% of consumers received search results that were not relevant to what they were searching for on a retail website. And roughly 85% of consumers view a brand differently after an unsuccessful search, while 74% say they avoid websites where they’ve experienced search difficulties in the past.
Each year, retailers lose more than $300 billion dollars from search abandonment, or when a consumer searches for a product on a retailer’s website but does not find what they are looking for. Our product discovery solutions help you surface the right products, to the right customers, at the right time. These solutions include:
- Vision Product Search, which is like bringing the augmented reality of Google Lens to a retailer’s own branded mobile app experience. Both shoppers and retail store associates can search for products using an image they’ve photographed or found online and receive a ranked list of similar items.
- Recommendations AI, which enables retailers to deliver highly personalized recommendations at scale across channels.
- Retail Search, which provides Google-quality search results on a retailer’s own website and mobile applications.
All three are powered by Google Cloud, leveraging Google’s advanced understanding of user context and intent, utilizing technology to deliver a seamless experience to every shopper. With these combined capabilities, retailers are able to reduce search abandonment and improve conversions across their digital properties.
Pivot point 3: Building the modern store
Stores are no longer places for just browsing and buying. They must be flexible operation centers, ready to pivot to address changing circumstances. The modern store must be multiple things at once: a mini-fulfillment and return center, a recommendation engine, a shopping destination, a fun place to work, and more.
Just as retail companies had to embrace omnichannel, stores are now becoming omnichannel centers on their own, mixing the digital and physical into a single location. Retailers can use physical stores as a vehicle to deliver superior customer experiences. This will demand heightened levels of collaboration and cooperation between stores, digital, and tech infrastructure teams, building on the agile ways they have worked together.
In many ways, it’s about allowing our physical spaces to function more like digital ones. Google Cloud can help by bringing the scalability, security, and reliability of the cloud to the store, allowing physical locations to upgrade infrastructure and modernize their internal and customer-facing applications.
Think of it as when a new OS gets released for your phone. It’s the same small, hard box, but the experience can feel radically different. Now, extend that same idea to a digitally enabled store. With the right displays, interfaces, and tools at a given retail location, the team only needs to send an over-the-air update to create radically fresh experiences, ranging from sales displays to fulfillment or employee engagement.
Such an approach can enable streamlined experiences for both customers and store associates. For instance, when it comes to the modern store’s evolving role as a fulfillment or return center, cloud solutions can help drive efficiency in stores through automation of ordering, replenishment, and fulfillment of omnichannel order selection.
Similar tools for personalized product discovery online can be applied to customers in the store, helping them to browse and explore, or even create a tailored shopping experience.
The impact of store associates can be maximized by equipping them with technology to provide expertise that drives value-added customer service, as well as increasing productivity in stores by streamlining operations, thus lowering overhead cost. At the register, customers should be able to enjoy frictionless checkout while ensuring reliable, accurate, secure transactions.
Google Cloud can help retailers transform
The ability to adapt and pivot to meet today’s changing consumer needs requires that retailers rely on modern tools to obtain operational flexibility. We believe that every company can be a tech company. That every decision is data driven. That every store is physical and digital all at once. That every worker is a tech worker.
Google Cloud works with retailers to help them solve their most challenging problems. We have the unique ability to handle massive amounts of unstructured data, in addition to advanced capabilities in AI and ML. Our products and solutions help retailers focus on what’s most important—from improving operations to capturing digital and omnichannel revenue.
ML Workflow Made Simple: How to Automate ML Experiment Tracking with Vertex AI Experiments Autologging
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Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance.
But tracking experiments is challenging: you need to organize experiments so that other team members can quickly understand, reproduce and compare them. That adds overhead that you don’t need.
We are happy to announce Vertex AI Experiments autologging, a solution which provides automated experiment tracking for your models, which streamlines your ML experimentation
With Vertex AI Experiments autologging, you can now log parameters, performance metrics and lineage artifacts by adding one line of code to your training script without needing to explicitly call any other logging methods.
How to use Vertex AI autologging
As a data scientist or ML practitioner, you conduct your experiment in a notebook environment such as Colab or Vertex AI Workbench. To enable Vertex AI Experiments autologging, you call aiplatform.autolog() in your Vertex AI Experiment session. After that call, any parameters, metrics and artifacts associated with model training are automatically logged and then accessible within the Vertex AI Experiment console.
Here’s how to enable autologging in your training session with a Scikit-learn model.
# Enable autologging
aiplatform.autolog()
# Build training pipeline
ml_pipeline = Pipeline(...)
# Train model
ml_pipeline.fit(x_train, y_train)This video shows parameters and training/post-training metrics in the Vertex AI Experiment console.
Vertex AI SDK autologging uses MLFlow’s autologging in its implementation and it supports several frameworks including XGBoost, Keras and Pytorch Lighting. See documentation for all supported frameworks.
Vertex AI Experiments autologging automatically logs model time series metrics when you train models along multiple epochs. That’s because of the integration between Vertex AI Experiments autologging and Vertex AI Tensorboard.
Furthermore, you can adapt Vertex AI Experiments autologging to your needs. For example, let’s say your team has a specific experiment naming convention. By default, Vertex AI Experiments autologging automatically creates Experiment Runs for you without requiring you to call `aiplatform.start_run()` or `aiplatform.end_run()`. If you’d like to specify your own Experiment Run names for autologging, you can manually initialize a specific run within the experiment using aiplatform.start_run() and aiplatform.end_run() after autologging has been enabled.
What’s next
You can access Vertex AI Experiments autologging with the latest version of Vertex AI SDK for Python. To learn more, check out these resources :
- Documentation: Autolog data to an experiment run
- Github: Get started with Vertex AI Experiments autologging
While I’m thinking about the next blog post, let me know if there is Vertex AI content you’d like to see on Linkedin or Twitter.
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Responsible AI: From Theory to Practice
In less than 10 years, AI will be the number one driver of global GDP growth. And organizations that achieve AI absorption will be the leaders of the global economy.
But as fast as AI is progressing, it also requires more care and attention from a responsibility standpoint. A more accurate and powerful vision AI technology, for example, when used in a harmful way can lead to harmful and intentional misuse, unintentional failure modes, and loss of personal privacy contributing to severe, real-life consequences for individuals.
Listen to Tracy Frey, Director, Product Strategy & Operations, Cloud AI – Google Cloud discuss implementing AI Principles into well-known products. Hear approaches in place for applying AI Principles in the product development process, including user research, product design, product reviews, testing, documentation, and marketing.
Costa Mesa Sanitary District Demonstrates How Public Utilities Can Leverage ML for Management and Upkeep of Manholes
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Local governments are embracing more modern and scalable ways to support their communities. In an effort to save both time and money, Costa Mesa Sanitary District (“CMSD”) used machine learning to automate and streamline manhole maintenance. Manhole maintenance is an essential part of the upkeep of cities. Manholes provide critical access points for underground public utilities, allowing inspection, maintenance, and system upgrades. But failure to upkeep manholes can cause a multitude of problems, from road hazards to sewer blockages, and can make it difficult for workers to access underground public utilities, which can lead to other safety issues. Manhole maintenance is an essential part of Costa Mesa Sanitary District maintenance, but this process requires the work of an outside consultant and costs the CMSD over $100,000.
ML to the rescue
CMSD, in collaboration with SpringML and Google Cloud, developed a project to streamline manhole maintenance by leveraging the power of machine learning (ML) to detect sewer manholes and rate their conditions. This solution saves CMSD $40,000 every year, freeing up funds for other public service projects.
Every quarter, one member of the CMSD drives a car outfitted with a GoPro camera. This car travels through the entire District area, which includes the city of Costa Mesa and small portions of Newport Beach, CA, which is about 218 street miles, and records the roads to detect approximately 5,000 manholes. At the end of the recording day, CMSD members transfer images and videos from the GoPro SD card into a local server. Then these files are automatically ingested into Google Cloud Storage for processing. From here, the machine learning algorithms detect which manholes need repair.
Google Cloud products are used throughout this project. Once the images and videos are in Google Cloud Storage, a workflow with Cloud Scheduler spins up the VM every night to detect if there are new videos on Cloud Storage. If there are, this triggers cloud machine learning, which reviews the numerous images and videos, rates each manhole, and determines if any require maintenance.
Machine learning to detect and grade manholes
SpringML applied a very systematic approach to detecting and grading the manholes. First, image processing ensures that the region of interest is only the section of the road in front of the vehicle to avoid any privacy concerns. Then SpringML applied a 5-step process using machine learning to detect and grade the manholes.
- SpringML developed two separate custom TensorFlow-based Mask R-CNN models. Mask-R-CNN is a deep neural network that is used for image segmentation tasks, which means it can separate different objects in an image or a video.
The first Mask-R-CNN model was created to accurately detect if an image showed a manhole cover. This was a critical step because sewer manhole covers look similar to water main covers. To make the model accurate, it was important that there be no false positives. SpringML used around 50 images of sewer manholes and other images to train and validate that the algorithm could successfully detect sewer manhole covers. - Once a manhole is accurately detected, the surrounding area is masked using image processing to focus on the manhole and then the cropped images are sent to the second model which is used to detect damage in the area around the manhole, called the apron. To reduce the processing time, the second model only does the inference if a manhole cover is detected.
- The second Mask-R-CNN model uses CMSD Guidelines to decide what types of damage contributed to the rating system. For training purposes, SpringML uses a TensorFlow-Keras inside a Virtual Machine on Google Cloud. The initial model was trained and the Keras weights were saved on Google Cloud Storage. This helped create a versioned system of the models as the model gets refined over time.
- Then, duplicated detections are cleaned up to have a unique detection for each manhole cover.
- Finally, Manholes are graded from a 1-5 rating, with 5 representing high damage and 1 representing low damage.
Once cloud machine learning has analyzed the new videos and images, the final scores are stored in BigQuery. Results are then served to members of CMSD via a simple web application where they can see which manholes need to be maintained. Two staff members review the ML results and determine priorities for repairs. One of the most interesting features of this project is that the model gets continually retrained based on feedback submitted in the web application. For example, if the model inaccurately detects a manhole, a member of CMSD can mark that in the web application and their feedback is immediately used to refine the model.
This solution shows how leveraging machine learning can streamline a necessary local government project and save money and labor while being highly scalable! Not only does this system streamline the manhole maintenance process, but it also allows for more frequent review of manhole conditions and provides a historical view of how the District’s manholes change over time.
Want to learn more about Google Cloud machine learning? Check out this tutorial to learn TensorFlow and Keras and check out more machine learning tools in our AI Platform.
Hike: Processing Analytics Queries 20X Faster with Google Cloud Platform
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After a seamless migration to Google Cloud Platform with CloudCover and Google Cloud Professional Services, Hike has reduced its costs by 20% and processed analytics queries 20 times faster than with its previous cloud provider. The business is also using AI and machine learning to enhance the experience provided by a new sticker-based messaging app, Hike Sticker Chat.
India is a market of opportunity for businesses that provide messaging apps to consumers. With more than 1.3 billion people, the country is the second most populous in the world. However, global messaging app providers face a robust market challenge from Hike, a home-grown internet and technology startup. Launched in 2012, Hike provides innovative products such as Hike Messenger and more recently the AI- and machine-learning-enabled Hike Sticker Chat, a service that enables young people in the country to express themselves through digital stickers.
The business says it understands the people of India and communication like no one else, while its mission is to reduce individuals’ dependency on the keyboard. To do this, Hike is building one of the largest repositories of AI and machine-learning-enabled stickers for Hike Sticker Chat. This messaging platform is, according to Hike, the only product of its type that enables conversations through stickers covering more than 40 languages and local dialects.
Google Cloud Results
- Processes analytics queries 20X faster than previously
- Doubles compute throughput
- Uses Google Cloud Machine Learning Engine managed, distributed capabilities to train complex models on TensorFlow that provide delightful local sticker recommendations through Hike Sticker Chat
Founded by Kavin Bharti Mittal, the Delhi-based venture is backed by SoftBank, Tencent, Tiger Global, Foxconn, and Bharti. To date, Hike has raised $261 million in funding. In August 2016, Hike raised its Series D round of funding, led by Tencent and Foxconn, at a valuation of $1.4 billion. The business is one of the fastest Indian startups to achieve Unicorn status, doing so in less than four years.
Hike started operations on a multinational cloud service. However, as user numbers and usage grew, the business began exploring options to improve performance and stability, reduce costs, and cut administration loads. In particular, Hike wanted to reduce latency between cloud data centers.
Focus on product development
“We aimed to move away from a technology stack with single points of failure to a horizontally scaled, highly reliable, distributed systems and managed services environment that enabled us to focus on product development rather than operations,” says Aditya Gupta, Director, Engineering, Hike.
Hike then began exploring the opportunities presented by Google Cloud Platform. The business held a number of executive-level meetings with Google to understand the capabilities, roadmap, and track record of the cloud service. It then decided to proceed with a proof of concept with Google Cloud Premier Partner CloudCover.
The proof of concept revealed that when Cloud Load Balancing was operating, latency between the Google Cloud data center in Taiwan and Delhi, India, was less than the latency between the incumbent cloud provider’s data center and Delhi. Further, compute throughput was up to two times greater on Compute Engine than on the equivalent service, while Hike could complete more then 1 million connections on Compute Engine – up from 500,000 connections on the incumbent service.
Migrate to GCP
The success of the exercise prompted Hike to migrate its messaging app to Google Cloud Platform. “We chose Google Cloud Platform because of its very broad set of services and features,” explains Gupta. “In addition, Google’s innovation mindset and the richness of the partnership would allow us to be onboarded quickly to machine learning services such as Cloud Machine Learning Engine.”
The business called on Google Cloud Professional Services (Technical Account Management) to help ensure a seamless lift-and-shift migration over two months. Google Cloud Professional Services initially undertook a technical infrastructure kickoff to establish a foundation for architecture requirements such as identity and access management and security.
Google Cloud Professional Services team delivers smooth migration
Google Cloud Professional Services worked closely with Hike to map out and deliver the Google Cloud Platform architecture that would deliver the greatest value to the business. The Professional Services team also worked with Hike to resolve product and support queries quickly; provided project background for product and support teams; and organized project meetings and early adopter program access.
In addition, Professional Services team members worked on site at least once a week, coordinated external support during critical migration periods, and coordinated teams in five countries for a single, 17-hour migration marathon. Over 60 days, the business migrated 7,000 processor cores, running virtual machine instances used for messaging infrastructure and analytics, to Google Cloud Platform.
Throughout the exercise, Google Cloud Professional Services worked with CloudCover to educate the customers’ technology teams to achieve proficiency with Google Cloud Platform. The teams soon built up skills and knowledge of best practices and began applying them to the Google Cloud Platform environment.
The Hike Google Cloud Platform architecture comprises virtual machine instances running in Compute Engine; Cloud Storage for unified object storage; networking; a BigQuery analytics data warehouse; Cloud Dataflow to transform and enrich data; Cloud Load Balancing to distribute workloads to maximize efficiency; and Cloud Dataproc to run Hadoop clusters.
Hike is also stepping up its AI & machine learning capabilities. It uses Google Cloud Machine Learning Engine managed, distributed computing capabilities to train complex models on TensorFlow. This powers key use cases such as delightful local sticker recommendations on Hike Sticker Chat. Hike is also investing heavily on AI and machine learning research.
Hike has achieved a range of benefits from its Google Cloud Platform deployment. As well as reduced latency, improved compute throughput, and increased connection handling, Google Cloud Platform managed services have enabled the business to reduce the time and effort required to administer core infrastructure, with the saved resources allocated to improving its messaging product.
“Managed services are beginning to reduce our operational overheads,” says Gupta. “For example, managed instance groups and Cloud Load Balancing are reducing our instance count and costs, thereby reducing involvement from DevOps and developer teams.”
Google Cloud Platform 20% cheaper
Gupta and his team have calculated that running for three years on Google Cloud Platform will cost, including the cost of migration, 20 percent less than on its previous platform. BigQuery is processing queries 20 times faster than a similar service offered by the previous provider, while storing 125TB of data and streaming 1.5TB of data daily. Furthermore, Hike’s analytics pipeline costs 80 percent less than in its previous environment.
“Google Cloud Platform has played an important role in enabling us to continue to innovate and realize our mission of reducing dependency on the keyboard,” says Gupta.
KLM’s Doubles Bookings With the Same Spend With Machine Learning
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GOALS
- Develop smarter, more effective media buying models through data
- Drive relevant advertising
- Scale predictive modelling across all touchpoints in the customer journey
APPROACH
Combined contextual data to create a predictive model with granular layers
Activated data in real-time
RESULTS
- 40% lower cost per booking
- More than twice as many bookings at same spend
- 1.4 times higher click-through rate for test group than control
KLM partnered with Relay42, whose data management platform (DMP) empowers marketers to achieve data-driven personalisation at scale.
The Relay42 DMP works by stitching together all touchpoints and data sources (including all Google solutions), orchestrating customer journeys in real-time and then activating the unified data to drive business and user experience results.
Relay42 and KLM created a data flow setup with the Relay42 DMP at the core. Thanks to Relay42’s tag management system, all customer interactions on KLM’s website and app, as well as relevant indicators from other channels and data sources such as email, social, CRM, call center and affiliates can be tracked and synced in Google Analytics 360.
Data gathered via Relay42’s own tags and DoubleClick Floodlight tags can also be sent from the DMP to the DoubleClick platform so that relevant ads can be targeted to appropriate audiences.
In order to perform deep-dive analyses, KLM simply exports raw-level data (from DoubleClick using Data Transfer and from Google Analytics 360 through seamless integration) to BigQuery. BigQuery can then correlate site behaviour with ad impressions, which enables Relay42 to activate the data and inform the build of predictive models.
“We believe that data in combination with technological innovation is bound to make advertising smarter and more relevant on every touchpoint. By combining data sources, leveraging first-party data and activating this in real-time, advertising can turn into a personal dialogue, a rewarding one-to-one interaction instead of one-to-many push messaging.”
– Kevin Duijndam, Cross Channel Marketing Manager, KLM
A Predictive Model to Improve Display Remarketing
KLM decided to test a new approach to remarketing.
“Our goal was to get rid of irrelevant ads, as they are simply annoying”, explains Kevin Duijndam, the airline’s Cross Channel Marketing Manager.
“Our assumption was that people who fly with us often already know us, so it would be irrelevant to tell them about flying with us again. However, we were wondering when exactly someone is a ‘frequent flyer’. The more we thought about it, the more complex the set of business rules became, so in the end we realized we couldn’t just focus on frequent flyers, but instead should use machine learning to understand when ads are irrelevant.”
KLM developed a real-time buying setup to include predictive modelling. In this setup, website and app interactions are tracked in the DMP thanks to the Relay42 tag management system.
Relevant consumer behaviour can be streamed in real-time to a prediction engine developed by KLM in the Google Cloud Platform, with the outputs then streamed straight back to the DMP. From here, the DMP can activate rule-based segments based on the prediction outcome. And by syncing this with DoubleClick, ads can be served and targeted to maximise relevance.
The team tested their new predictive model to assess any gains in performance. KLM deliberately chose to measure the results in an A/B setup within a defined period rather than measuring the differences year over year or month over month. Such comparisons are less reliable due to rapid changes relating to seasonality, internal capacities and external factors caused by competitors.
Business Gains and Customer Experience Wins
In the test, KLM linked the Relay42 DMP customer interaction data to their predictive model to predict how relevant their ads would be. The setup enabled a decision to be made in real-time whether or not to serve a specific ad to a user.
Through the A/B tests it became clear that the new model in fact did generate a significant uplift in bookings. With the cost per booking 40% lower during the test period, KLM was able to achieve more than twice as many bookings at the same spend.
The test produced wins in terms of customer experience, too. The click-through rate for the test group was more than 1.4 times higher than for the control group, indicating that the new model was successfully reaching users with messages they found to be relevant rather than annoying.
The success goes beyond improving KLM’s display remarketing efforts, though.
“Even more importantly, we’ve laid the IT data flow foundation in such a way that KLM is now able to execute on our data through all of our digital marketing channels and apply our prediction models at scale”, Kevin says. “So we can be flexible to plug in other models but can also now scale through other online media channels like search or video.”
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